X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/549adc5739f5afe33d12e1afd1b309896839f71d..51c03eb8a38bbfba54b1e2fadca7ab7b1db166b3:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index fd2b6ff..7d96f2b 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -82,8 +82,8 @@ what parameters  could influence or not  the behaviors of an  algorithm. In this
 paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
 of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
 synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
-simulations  in  which we  choose  some parameters.   Both  codes  are real  MPI
-codes. Simulations allow us to see when the multisplitting algorithm can be more
+simulations  which let us easily choose  some parameters.   Both  codes  are real  MPI
+codes ans simulations allow us to see when the asynchronous multisplitting algorithm can be more
 efficient than the GMRES one to solve a 3D Poisson problem.
 
 
@@ -695,9 +695,11 @@ In this work, we show that SIMGRID is an efficient simulation tool that allows u
 reach the following three objectives: 
 
 \begin{enumerate}
-\item To have a flexible configurable execution platform resolving the 
-hard exercise to access to very limited but so solicited physical 
-resources;
+\item  To have  a flexible  configurable execution  platform that  allows  us to
+  simulate asynchronous iterative algorithm for  which execution of all parts of
+  the  code is  necessary. Using  simulations before  real execution  is  a nice
+  solution to detect the scalability problems.
+
 \item to ensure the algorithm convergence with a reasonable time and
 iteration number ;
 \item and finally and more importantly, to find the correct combination